Since the start of the Sentinel-1 (S-1) mission, numerous cases of severe image degradation caused by radio frequency interference (RFI) have been reported, which puts forward an urgent need for RFI identification and mitigation. In this article, an automatic RFI identification method is proposed based on a Siamese-type deep convolutional neural network (Siam-CNN-RIM). The Siam-CNN-RIM can be served as a preprocessing step before RFI mitigation to identify whether an S-1 image is RFI-contaminated or not. Different from traditional RFI identification networks which only use a single image as input, an additional image in the repeat-pass time series is also fed into the input of Siam-CNN-RIM as a reference. Both the input images correspond to the same illuminated area and pass through the same convolutional layer followed by an energy function such that the different features caused by RFI can be extracted and the background terrain features can be ignored. This is beneficial for distinguishing the real RFI signatures and the similar terrain signatures that may cause false positives and thus improving the RFI identification performance. Experimental results show that the proposed method is robust in different scenarios and can achieve more than 97% RFI identification accuracy, even for the open-set task where the test scenarios are not included in the training set.

中文翻译：

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基于连体型 Deep CNN 的 Sentinel-1 自动 RFI 识别

自 Sentinel-1 (S-1) 任务开始以来，已报告了多起由射频干扰 (RFI) 引起的严重图像质量下降的案例，这对识别和缓解 RFI 提出了迫切需要。本文提出了一种基于连体型深度卷积神经网络（Siam-CNN-RIM）的射频干扰自动识别方法。Siam-CNN-RIM 可以用作 RFI 缓解之前的预处理步骤，以识别 S-1 图像是否受到 RFI 污染。与仅使用单个图像作为输入的传统 RFI 识别网络不同，重复通过时间序列中的附加图像也被馈送到 Siam-CNN-RIM 的输入作为参考。两幅输入图像对应同一个光照区域，经过同一个卷积层后跟一个能量函数，这样就可以提取RFI引起的不同特征，而忽略背景地形特征。这有利于区分真实的RFI特征和可能导致误报的相似地形特征，从而提高RFI识别性能。实验结果表明，该方法在不同场景下具有鲁棒性，即使对于训练集中不包含测试场景的开放集任务，RFI识别准确率也能达到97%以上。这有利于区分真实的RFI特征和可能导致误报的相似地形特征，从而提高RFI识别性能。实验结果表明，该方法在不同场景下具有鲁棒性，即使对于训练集中不包含测试场景的开放集任务，RFI识别准确率也能达到97%以上。这有利于区分真实的RFI特征和可能导致误报的相似地形特征，从而提高RFI识别性能。实验结果表明，该方法在不同场景下具有鲁棒性，即使对于训练集中不包含测试场景的开放集任务，RFI识别准确率也能达到97%以上。